Bacterial start site prediction.

With the growing number of completely sequenced bacterial genes, accurate gene prediction in bacterial genomes remains an important problem. Although the existing tools predict genes in bacterial genomes with high overall accuracy, their ability to pinpoint the translation start site remains unsatisfactory. In this paper, we present a novel approach to bacterial start site prediction that takes into account multiple features of a potential start site, viz., ribosome binding site (RBS) binding energy, distance of the RBS from the start codon, distance from the beginning of the maximal ORF to the start codon, the start codon itself and the coding/non-coding potential around the start site. Mixed integer programing was used to optimize the discriminatory system. The accuracy of this approach is up to 90%, compared to 70%, using the most common tools in fully automated mode (that is, without expert human post-processing of results). The approach is evaluated using Bacillus subtilis, Escherichia coli and Pyrococcus furiosus. These three genomes cover a broad spectrum of bacterial genomes, since B.subtilis is a Gram-positive bacterium, E.coli is a Gram-negative bacterium and P. furiosus is an archaebacterium. A significant problem is generating a set of 'true' start sites for algorithm training, in the absence of experimental work. We found that sequence conservation between P. furiosus and the related Pyrococcus horikoshii clearly delimited the gene start in many cases, providing a sufficient training set.     

GS-Finder: a program to find bacterial gene start sites with a self-training method

In this paper, a self-training method is proposed to recognize translation start sites in bacterial genomes without a prior knowledge of rRNA in the genomes concerned. Many features with biological meanings are incorporated, including mononucleotide distribution patterns near the start codon, the start codon itself, the coding potential and the distance from the most-left start codon to the start codon. The proposed method correctly predicts 92% of the translation start sites of 195 experimentally confirmed Escherichia coli CDSs, 96% of 58 reliable Bacillus subtilis CDSs and 82% of 140 reliable Synechocystis CDSs. Moreover, the self-training method presented might also be used to relocate the translation start sites of putative CDSs of genomes, which are predicted by gene-finding programs. After post-processing by the method presented, the improvement of gene start prediction of some gene-finding programs is remarkable, e.g., the accuracy of gene start prediction of Glimmer 2.02 increases from 63 to 91% for 832 E. coli reliable CDSs. An open source computer program to implement the method, GS-Finder, is freely available for academic purposes from http://tubic.tju.edu.cn/GS-Finder/.     

Genome information management and integrated data analysis with HaloLex

HaloLex is a software system for the central management, integration, curation, and web-based visualization of genomic and other -omics data for any given microorganism. The system has been employed for the manual curation of three haloarchaeal genomes, namely Halobacterium salinarum (strain R1), Natronomonas pharaonis, and Haloquadratum walsbyi. HaloLex, in particular, enables the integrated analysis of genome-wide proteomic results with the underlying genomic data. This has proven indispensable to generate reliable gene predictions for GC-rich genomes, which, due to their characteristically low abundance of stop codons, are known to be hard targets for standard gene finders, especially concerning start codon assignment. The proteomic identification of more than 600 N-terminal peptides has greatly increased the reliability of the start codon assignment for Halobacterium salinarum. Application of homology-based methods to the published genome of Haloarcula marismortui allowed to detect 47 previously unidentified genes (a problem that is particularly serious for short protein sequences) and to correct more than 300 start codon misassignments.     

Correlations between Shine-Dalgarno sequences and gene features such as predicted expression levels and operon structures

This work assesses relationships for 30 complete prokaryotic genomes between the presence of the Shine-Dalgarno (SD) sequence and other gene features, including expression levels, type of start codon, and distance between successive genes. A significant positive correlation of the presence of an SD sequence and the predicted expression level of a gene based on codon usage biases was ascertained, such that predicted highly expressed genes are more likely to possess a strong SD sequence than average genes. Genes with AUG start codons are more likely than genes with other start codons, GUG or UUG, to possess an SD sequence. Genes in close proximity to upstream genes on the same coding strand in most genomes are significantly higher in SD presence. In light of these results, we discuss the role of the SD sequence in translation initiation and its relationship with predicted gene expression levels and with operon structure in both bacterial and archaeal genomes.     

A probabilistic method for identifying start codons in bacterial genomes.

As the pace of genome sequencing has accelerated, the need for highly accurate gene prediction systems has grown. Computational systems for identifying genes in prokaryotic genomes have sensitivities of 98-99% or higher (Delcher et al., Nucleic Acids Res., 27, 4636-4641, 1999). These accuracy figures are calculated by comparing the locations of verified stop codons to the predictions. Determining the accuracy of start codon prediction is more problematic, however, due to the relatively small number of start sites that have been confirmed by independent, non-computational methods. Nonetheless, the accuracy of gene finders at predicting the exact gene boundaries at both the 5' and 3' ends of genes is of critical importance for microbial genome annotation, especially in light of the important signaling information that is sometimes found on the 5' end of a protein coding region. In this paper we propose a probabilistic method to improve the accuracy of gene identification systems at finding precise translation start sites. The new system, RBSfinder, is tested on a validated set of genes from Escherichia coli, for which it improves the accuracy of start site locations predicted by computational gene finding systems from the range 67-77% to 90% correct.     

Validation of a <Emphasis Type="Italic">Burkholderia pseudomallei</Emphasis> Hypothetical Protein and Determination of Its Translational Start Codon Using Chromosomal Integration of His-Tag Coding Sequence

In this post genomic era, there are a great number of in silico annotated hypothetical genes. However, experimental validation of the functionality of these genes remains tentative. Two of the major challenges faced by researcher are whether these hypothetical genes are protein-coding genes and whether their corresponding predicted translational start codons are correct. In this report, we demonstrate a convenient procedure to validate the presence of a hypothetical gene product of BPSS1356 from Burkholderia pseudomallei as well as its start codon. It was done by integration of a His-Tag coding sequence into C-terminal end of BPSS1356 gene via homologous recombination. We then purified the native protein using affinity chromatography. The genuine start codon of BPSS1356 was then determined by protein N-terminal sequencing.     

Large-scale prokaryotic gene prediction and comparison to genome annotation

MOTIVATION: Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to propagation of errors when questionable assignments are adapted from one genome to another. Genome comparison either on a large or small scale would be facilitated by using a single standard for annotation, which incorporates a transparency of why an open reading frame (ORF) is considered to be a gene. RESULTS: A total of 143 prokaryotic genomes were scored with an updated version of the prokaryotic genefinder EasyGene. Comparison of the GenBank and RefSeq annotations with the EasyGene predictions reveals that in some genomes up to approximately 60% of the genes may have been annotated with a wrong start codon, especially in the GC-rich genomes. The fractional difference between annotated and predicted confirms that too many short genes are annotated in numerous organisms. Furthermore, genes might be missing in the annotation of some of the genomes. We predict 41 of 143 genomes to be over-annotated by >5%, meaning that too many ORFs are annotated as genes. We also predict that 12 of 143 genomes are under-annotated. These results are based on the difference between the number of annotated genes not found by EasyGene and the number of predicted genes that are not annotated in GenBank. We argue that the average performance of our standardized and fully automated method is slightly better than the annotation.     

Finding prokaryotic genes by the 'frame-by-frame' algorithm: targeting gene starts and overlapping genes

MOTIVATION: Tightly packed prokaryotic genes frequently overlap with each other. This feature, rarely seen in eukaryotic DNA, makes detection of translation initiation sites and, therefore, exact predictions of prokaryotic genes notoriously difficult. Improving the accuracy of precise gene prediction in prokaryotic genomic DNA remains an important open problem. RESULTS: A software program implementing a new algorithm utilizing a uniform Hidden Markov Model for prokaryotic gene prediction was developed. The algorithm analyzes a given DNA sequence in each of six possible global reading frames independently. Twelve complete prokaryotic genomes were analyzed using the new tool. The accuracy of gene finding, predicting locations of protein-coding ORFs, as well as the accuracy of precise gene prediction, and detecting the whole gene including translation initiation codon were assessed by comparison with existing annotation. It was shown that in terms of gene finding, the program performs at least as well as the previously developed tools, such as GeneMark and GLIMMER. In terms of precise gene prediction the new program was shown to be more accurate, by several percentage points, than earlier developed tools, such as GeneMark.hmm, ECOPARSE and ORPHEUS. The results of testing the program indicated the possibility of systematic bias in start codon annotation in several early sequenced prokaryotic genomes. AVAILABILITY: The new gene-finding program can be accessed through the Web site: http:@dixie.biology.gatech.edu/GeneMark/fbf.cgi CONTACT: mark@amber.gatech.edu.     

Dictionary-driven prokaryotic gene finding

Gene identification, also known as gene finding or gene recognition, is among the important problems of molecular biology that have been receiving increasing attention with the advent of large scale sequencing projects. Previous strategies for solving this problem can be categorized into essentially two schools of thought: one school employs sequence composition statistics, whereas the other relies on database similarity searches. In this paper, we propose a new gene identification scheme that combines the best characteristics from each of these two schools. In particular, our method determines gene candidates among the ORFs that can be identified in a given DNA strand through the use of the Bio-Dictionary, a database of patterns that covers essentially all of the currently available sample of the natural protein sequence space. Our approach relies entirely on the use of redundant patterns as the agents on which the presence or absence of genes is predicated and does not employ any additional evidence, e.g. ribosome-binding site signals. The Bio-Dictionary Gene Finder (BDGF), the algorithm’s implementation, is a single computational engine able to handle the gene identification task across distinct archaeal and bacterial genomes. The engine exhibits performance that is characterized by simultaneous very high values of sensitivity and specificity, and a high percentage of correctly predicted start sites. Using a collection of patterns derived from an old (June 2000) release of the Swiss-Prot/TrEMBL database that contained 451 602 proteins and fragments, we demonstrate our method’s generality and capabilities through an extensive analysis of 17 complete archaeal and bacterial genomes. Examples of previously unreported genes are also shown and discussed in detail.     

Improving the specificity of exon prediction using comparative genomics

Background

Computational gene prediction tools routinely generate large volumes of predicted coding exons (putative exons). One common limitation of these tools is the relatively low specificity due to the large amount of non-coding regions.

Methods

A statistical approach is developed that largely improves the gene prediction specificity. The key idea is to utilize the evolutionary conservation principle relative to the coding exons. By first exploiting the homology between genomes of two related species, a probability model for the evolutionary conservation pattern of codons across different genomes is developed. A probability model for the dependency between adjacent codons/triplets is added to differentiate coding exons and random sequences. Finally, the log odds ratio is developed to classify putative exons into the group of coding exons and the group of non-coding regions.

Results

The method was tested on pre-aligned human-mouse sequences where the putative exons are predicted by GENSCAN and TWINSCAN. The proposed method is able to improve the exon specificity by 73% and 32% respectively, while the loss of the sensitivity ≤ 1%. The method also keeps 98% of RefSeq gene structures that are correctly predicted by TWINSCAN when removing 26% of predicted genes that are in non-coding regions. The estimated number of true exons in TWINSCAN's predictions is 157,070. The results and the executable codes can be downloaded from http://www.stat.purdue.edu/~jingwu/codon/

Conclusion

The proposed method demonstrates an application of the evolutionary conservation principle to coding exons. It is a complementary method which can be used as an additional criteria to refine many existing gene predictions.

     

A Method for Identification of Selenoprotein Genes in Archaeal Genomes

The genetic codon UGA has a dual function: serving as a terminator and encoding selenocysteine. However, most popular gene annotation programs only take it as a stop signal, resulting in misannotation or completely missing selenoprotein genes. We developed a computational method named Asec-Prediction that is specific for the prediction of archaeal selenoprotein genes. To evaluate its effectiveness, we first applied it to 14 archaeal genomes with previously known selenoprotein genes, and Asec-Prediction identified all reported selenoprotein genes without redundant results. When we applied it to 12 archaeal genomes that had not been researched for selenoprotein genes, Asec-Prediction detected a novel selenoprotein gene in Methanosarcina acetivorans. Further evidence was also collected to support that the predicted gene should be a real selenoprotein gene. The result shows that Asec-Prediction is effective for the prediction of archaeal selenoprotein genes.     

A Universal Trend of Reduced mRNA Stability near the Translation-Initiation Site in Prokaryotes and Eukaryotes

Recent studies have suggested that the thermodynamic stability of mRNA secondary structure near the start codon can regulate translation efficiency in Escherichia coli, and that translation is more efficient the less stable the secondary structure. We survey the complete genomes of 340 species for signals of reduced mRNA secondary structure near the start codon. Our analysis includes bacteria, archaea, fungi, plants, insects, fishes, birds, and mammals. We find that nearly all species show evidence for reduced mRNA stability near the start codon. The reduction in stability generally increases with increasing genomic GC content. In prokaryotes, the reduction also increases with decreasing optimal growth temperature. Within genomes, there is variation in the stability among genes, and this variation correlates with gene GC content, codon bias, and gene expression level. For birds and mammals, however, we do not find a genome-wide trend of reduced mRNA stability near the start codon. Yet the most GC rich genes in these organisms do show such a signal. We conclude that reduced stability of the mRNA secondary structure near the start codon is a universal feature of all cellular life. We suggest that the origin of this reduction is selection for efficient recognition of the start codon by initiator-tRNA.     

FragGeneScan: predicting genes in short and error-prone reads

The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved ∼62% for reads of 400 bases with 1% sequencing errors, and ∼18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database.     

A hybrid gene team model and its application to genome analysis

It is well-known that functionally related genes occur in a physically clustered form, especially operons in bacteria. By leveraging on this fact, there has recently been an interesting problem formulation known as gene team model, which searches for a set of genes that co-occur in a pair of closely related genomes. However, many gene teams, even experimentally verified operons, frequently scatter within other genomes. Thus, the gene team model should be refined to reflect this observation. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraints. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. We also compared the result from our hybrid model to those from the traditional gene team model. We also show that predicted gene teams can be used for various genome analysis: operon prediction, phylogenetic analysis of organisms, contextual sequence analysis and genome annotation. Our program is fast enough to provide a service on the web at http://platcom.informatics.indiana.edu/platcom/. Users can select any combination of 97 genomes to predict gene teams.     

Genome-wide comparative gene family classification

Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species.     

Translation of the leaderless Caulobacter dnaX mRNA.

The expression of the Caulobacter crescentus homolog of dnaX, which in Escherichia coli encodes both the gamma and tau subunits of the DNA polymerase III holoenzyme, is subject to cell cycle control. We present evidence that the first amino acid in the predicted DnaX protein corresponds to the first codon in the mRNA transcribed from the dnaX promoter; thus, the ribosome must recognize the mRNA at a site downstream of the start codon in an unusual but not unprecedented fashion. Inserting four bases in front of the AUG at the 5' end of dnaX mRNA abolishes translation in the correct frame. The sequence upstream of the translational start site shows little homology to the canonical Shine-Dalgarno ribosome recognition sequence, but the region downstream of the start codon is complementary to a region of 16S rRNA implicated in downstream box recognition. The region downstream of the dnaX AUG, which is important for efficient translation, exhibits homology with the corresponding region from the Caulobacter hemE gene adjacent to the replication origin. The hemE gene also appears to be translated from a leaderless mRNA. Additionally, as was found for hemE, an upstream untranslated mRNA also extends into the dnaX coding sequence. We propose that translation of leaderless mRNAs may provide a mechanism by which the ribosome can distinguish between productive and nonproductive templates.     

Picky: oligo microarray design for large genomes

MOTIVATION: Many large genomes are getting sequenced nowadays. Biologists are eager to start microarray analysis taking advantage of all known genes of a species, but existing microarray design tools were very inefficient for large genomes. Also, many existing tools operate in a batch mode that does not assure best designs. RESULTS: Picky is an efficient oligo microarray design tool for large genomes. Picky integrates novel computer science techniques and the best known nearest-neighbor parameters to quickly identify sequence similarities and estimate their hybridization properties. Oligos designed by Picky are computationally optimized to guarantee the best specificity, sensitivity and uniformity under the given design constrains. Picky can be used to design arrays for whole genomes, or for only a subset of genes. The latter can still be screened against a whole genome to attain the same quality as a whole genome array, thereby permitting low budget, pathway-specific experiments to be conducted with large genomes. Picky is the fastest oligo array design tool currently available to the public, requiring only a few hours to process large gene sets from rice, maize or human.